2020 年 1-4 月全球 COVID-19 传播模式的核算。

Yothin Jinjarak, Rashad Ahmed, Sameer Nair-Desai, Weining Xin, Joshua Aizenman
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摘要

建立大流行病模型和指导决策的关键因素包括:与感染相关的死亡率;政府政策、医疗系统和社会适应大流行病动态变化的能力;以及影响公民对严格政策的看法和行为反应的机构和人口特征。本文追踪了 COVID-19 死亡率、旨在限制社会接触的政策干预之间的跨国关联,以及它们与制度和人口特征之间的相互作用。我们发现,在滞后情况下,更严格的大流行病政策与较低的死亡率增长率相关。在老年人口和城市人口比例较大、民主自由程度较高、国际旅行流量较大的国家,更严格的大流行病政策与较低的未来死亡率增长率之间的关联更为明显。在首次死亡前制定了更严格政策的国家,其死亡率峰值较低,首次死亡峰值的持续时间也较短。与此相反,初始流动性较高的国家在大流行病第一阶段的死亡率峰值较高,而老年人口较多,易受感染职业的雇员比例较高,民主程度较高的国家达到死亡率峰值的时间较长。我们的研究结果表明,政策干预能够有效减缓死亡率的几何增长模式、降低死亡率峰值并缩短达到第一个峰值的时间。我们还揭示了制度和人口特征在指导未来大流行病政策制定方面的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Accounting for Global COVID-19 Diffusion Patterns, January-April 2020.

Key factors in modeling a pandemic and guiding policy-making include mortality rates associated with infections; the ability of government policies, medical systems, and society to adapt to the changing dynamics of a pandemic; and institutional and demographic characteristics affecting citizens' perceptions and behavioral responses to stringent policies. This paper traces the cross-country associations between COVID-19 mortality, policy interventions aimed at limiting social contact, and their interactions with institutional and demographic characteristics. We document that, with a lag, more stringent pandemic policies were associated with lower mortality growth rates. The association between stricter pandemic policies and lower future mortality growth is more pronounced in countries with a greater proportion of the elderly population and urban population, greater democratic freedoms, and larger international travel flows. Countries with greater policy stringency in place prior to the first death realized lower peak mortality rates and exhibited lower durations to the first mortality peak. In contrast, countries with higher initial mobility saw higher peak mortality rates in the first phase of the pandemic, and countries with a larger elderly population, a greater share of employees in vulnerable occupations, and a higher level of democracy took longer to reach their peak mortalities. Our results suggest that policy interventions are effective at slowing the geometric pattern of mortality growth, reducing the peak mortality, and shortening the duration to the first peak. We also shed light on the importance of institutional and demographic characteristics in guiding policy-making for future waves of the pandemic.

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